From advanced visualization, collaboration, and reproducibility to data manipulation, R Day at Strata covers a raft of current topics that analysts and R users need to pay attention to. The R Day tutorials come from leading luminaries and R committers, the folks keeping the R ecosystem apace of the challenges facing analysts and others who work with data.
R Quickstart: Wrangle, transform, and visualize data
Instructor: Garrett Grolemund
This 90 minute quickstart will teach you the most used–and most powerful–parts of the R language. You will learn the best ways to perform the core tasks of data science:
Work with Big Data in R
Instructor: Nathan Stephens
R is the go to language for data exploration and development, but what role can R play in production with big data? In this class, RStudio’s solution engineer demonstrates a pragmatic approach for pairing R with big data. You will learn to use R’s familiar dplyr syntax to query big data stored on a server based data store, like Amazon Redshift or Google BigQuery. We will also discuss how to generalize the process to other big data stores, and how to best leverage R within a big data pipeline.
Reproducible Reports with Big Data
Instructor: Yihui Xie
This tutorial will teach you a time-saving workflow that has become the new standard for reproducible research. The R Markdown package makes it easy to document both your code and your results in the same file. With an R Markdown file and the click of a button, you can
re-execute your analysis with the most up-to-date code and data to create new results, and/or
generate a polished report in a variety of formats (html, pdf, Word, etc.) to share your results
This class will demonstrate some best practices that further increase the efficiency of reproducible research with R Markdown.
Interactive Shiny Applications built on Big Data
Instructor: Garrett Grolemund
R’s Shiny package lets you move beyond static reports to easily build interactive applications powered by R. Run your Shiny apps locally, or share them over a server with clients, customers, and colleagues. Your visitors will be in the driver seat. They can explore data, monitor dashboards, run R analyses, or do anything else you prepare for them all without knowing any R code. If a picture is worth 1000 words, a Shiny app is worth a million. In this tutorial, you will learn the basics of creating Shiny apps, as well as the best practices for using big data with your apps.
Garrett Grolemund is the editor-in-chief of shiny.rstudio.com, the development center for the Shiny R package, and is the author of Hands-On Programming with R as well as Data Science with R, a forthcoming book by O’Reilly Media. Garrett works as a data scientist and chief instructor for RStudio, Inc.
Yihui Xie is an active R user and the author of several R packages, such as animation, formatR, Rd2roxygen, and knitr, among which the animation package won the 2009 John M. Chambers Statistical Software Award (ASA). He is also the author of the book Dynamic Documents with R and knitr. In 2006 he founded the “Capital of Statistics” (http://cos.name), which has grown into a large online community on statistics in China. He initiated the first Chinese R conference in 2008 and has been organizing R conferences in China since then. During his PhD training at the Iowa State University, he won the Vince Sposito Statistical Computing Award (2011) and the Snedecor Award (2012) in the Department of Statistics. His research interests include interactive statistical graphics, statistical computing, and web applications.
Nathan Stephens recently joined RStudio as director of solutions engineering. His background is in applied analytics and consulting. He has experience building data science teams, creating innovative data products, analyzing big data, and architecting analytic platforms. He was an early adopter of R and has introduced it into many organizations. Nathan holds an MS in statistics from Brigham Young University.
Randall Pruim is a professor of mathematics and statistics at Calvin College, author of Foundations and Applications of Statistics: An Introduction Using R, and the maintainer of several R packages, including fastR and mosaic. His research interests include statistical computing and statistics education (especially for students in the natural sciences).
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